Identifiability and Estimation of Causal Effects by Principal Stratification With Outcomes Truncated by Death

Ding, Peng; Geng, Zhi; Yan, Wei; Zhou, Xiao‐Hua

doi:10.1198/jasa.2011.tm10265

Cited by 72 publications

(156 citation statements)

References 18 publications

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“…Ding et al (2011) also considered identifiability of a principal effect when outcomes are truncated by death, which is mathematically identical to the problem considered here. In addition to Assumptions 1 and 2 above, Ding et al provided two additional assumptions which are sufficient for identifiability: (i) X i ⊥ Y i |{ S i (0), S i (1), Z i } and (ii) Pr[ X i = x | S i (0) = S i (1) = 0] ≠ Pr[ X i = x | S i (0) = 1, S i (1) = 0].…”

Section: Identifiabilitymentioning

confidence: 99%

Sharpening Bounds on Principal Effects with Covariates

Long

Hudgens

2013

Biometrics

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Summary Estimation of treatment effects in randomized studies is often hampered by possible selection bias induced by conditioning on or adjusting for a variable measured post-randomization. One approach to obviate such selection bias is to consider inference about treatment effects within principal strata, i.e., principal effects. A challenge with this approach is that without strong assumptions principal effects are not identifiable from the observable data. In settings where such assumptions are dubious, identifiable large sample bounds may be the preferred target of inference. In practice these bounds may be wide and not particularly informative. In this work we consider whether bounds on principal effects can be improved by adjusting for a categorical baseline covariate. Adjusted bounds are considered which are shown to never be wider than the unadjusted bounds. Necessary and sufficient conditions are given for which the adjusted bounds will be sharper (i.e., narrower) than the unadjusted bounds. The methods are illustrated using data from a recent, large study of interventions to prevent mother-to-child transmission of HIV through breastfeeding. Using a baseline covariate indicating low birth weight, the estimated adjusted bounds for the principal effect of interest are 63% narrower than the estimated unadjusted bounds.

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Section: Identifiabilitymentioning

confidence: 99%

Sharpening Bounds on Principal Effects with Covariates

Long

Hudgens

2013

Biometrics

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“…Second, researchers can restrict the relationship between a special covariate and the outcome; for example, assuming that the treatment effect does not vary across site (Raudenbush et al, 2012). While many such restrictions are possible (e.g., Jo, 2002;Ding et al, 2011;Mealli and Pacini, 2013), there is no clear candidate for such a special covariate in HSIS, nor is it plausible to assume that the treatment effect is constant across Head Start centers. Finally, see Hall and Zhou (2003) and Mealli and Pacini (2013) for assumptions when there are multiple, independent outcomes.…”

Section: Principal Stratificationmentioning

confidence: 99%

“…Second, parametric assumptions can often be more plausible conditional on covariates than marginally. For additional discussion, see Hirano et al (2000); Jo (2002); Jo and Stuart (2009); Ding et al (2011);Feller (2015).…”

Section: Covariatesmentioning

confidence: 99%

Compared to what? Variation in the impacts of early childhood education by alternative care type

Feller¹,

Grindal²,

Miratrix³

et al. 2016

Ann. Appl. Stat.

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Early childhood education research often compares a group of children who receive the intervention of interest to a group of children who receive care in a range of different care settings. In this paper, we estimate differential impacts of an early childhood intervention by alternative care setting, using data from the Head Start Impact Study, a large-scale randomized evaluation. To do so, we utilize a Bayesian principal stratification framework to estimate separate impacts for two types of Compliers: those children who would otherwise be in other center-based care when assigned to control and those who would otherwise be in home-based care. We find strong, positive short-term effects of Head Start on receptive vocabulary for those Compliers who would otherwise be in home-based care. By contrast, we find no meaningful impact of Head Start on vocabulary for those Compliers who would otherwise be in other center-based care. Our findings suggest that alternative care type is a potentially important source of variation in early childhood education interventions.

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“…4;10 An alternative strategy that is sometimes adopted entails performing a sensitivity analysis, 11 16 or bounds can sometimes be obtained. 17; 18 Zhang et al replace monotonicity with strong distributional assumptions, that the outcome is normally distributed within principal strata, a strategy which is of little use for binary outcome. 18 Another approach is given by Ding et al who assume a form of exclusion restriction for an observed pre-exposure covariate to obtain nonparametric identi…cation of SACE.…”

Section: Discussionmentioning

confidence: 99%

“…17; 18 Zhang et al replace monotonicity with strong distributional assumptions, that the outcome is normally distributed within principal strata, a strategy which is of little use for binary outcome. 18 Another approach is given by Ding et al who assume a form of exclusion restriction for an observed pre-exposure covariate to obtain nonparametric identi…cation of SACE. 19 Speci…cally, Ding et al assume that one has observed a pre-exposure correlate of survival, which is independent of the observed outcome conditional on principal strata.…”

Section: Discussionmentioning

confidence: 99%